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Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

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Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#81

Post by d_tr »

zamfi wrote: post #79
d_tr wrote: post #36

Earlier quoted context omitted.

This is very impressive, but since a biological brain is so much more complicated, who could really make a solid guess? Probably no one right now. PaLM is not an attempt at AGI, a parameter is not equivalent to a neural connection, an activation function is not equivalent to a neuron (of which you have many different types), biological connection patterns are much richer, and biological stimuli are not like slideshow…

I made no claims contrary to anything in your post; your response — none of which I disagree with — makes me worry that you are coming in with a preexisting belief and just looking for reasons it must be true. That said, there are plenty of multimodal networks (ie not slideshows), and we know very little about the relevance to intelligence of the “richness” of neural connections, activations, etc. — but it’s inarguab…

In your previous comment you seemed to suggest that we should not be very far. Maybe I misinterpreted you.

I do believe that AGI is possible and that it does not have to resemble a biological brain though.

Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#82

Post by Galaxeblaffer »

wrp wrote: post #68
Galaxeblaffer wrote: post #58

Randomly watched this yesterday https://www.youtube.com/watch?v=hXgqik6HXc0&ab_channel=LexFr... where Roger Penrose argues that we're missing something fundamental about consciousness and his best bet is a structure called the microtubules. This talk reminded me of my own research into "AI" back in the 00's and that it's almost impossible to talk about AI since everybody has a different idea as to whay AI is, yes i k…

Alwyn Scott's Stairway to the Mind (1995) has an accessible critique of Penrose's theory from the perspective of neurophysics. Basically, he argues that neuronal activity is on such a large time and energy scale that quantum effects are unlikely to be relevant.

it does sound quite out there, i agree. and i personally think that it's too early to conclude that algorithms with neutral nets won't get us there, we simply don't have enough computing power to conclude that yet. Penrose has always been a Maverick and as he admits himself he's not even close to an explanation himself. his only lead comes from the fact that anesthetic gases seem to have some kind of effect on the microtubules and i guess that in itself could have a totally different explanation than Quantum magic. i mean the micrtubules could be important but for different reasons.

Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#83

Post by rbanffy »

CuriouslyC wrote: post #66
rbanffy wrote: post #59

Remember growth is exponential - we won't recognize the next revolution because we'll still be dealing with the fallout of the previous one. Or previous dozen.

Incorrect. Any growth in a system of finite resources is sigmoidal, with an exponential portion early in the curve before diminishing returns kicks in.

We've been promised this since the 90's, and, yet, we've been pushing back the wall Moore's law was supposed to hit for more than 30 years. And we haven't even properly started to play with non-transistor logic and analog neural devices, so I am cautiously imagining we'll remain exponential for the time being.

Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#84

Post by guestbest »

goatlover wrote: post #17
guestbest wrote: post #4

We already have artificial intelligence. It’s called children

Children are natural intelligences. An existing artificial example would be corporations or governments, although it's not a singular/individual kind of intelligence, but rather an organizational one.

There is little intelligence in a corporation

Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#85

Post by sharemywin »

rbanffy wrote: post #83
CuriouslyC wrote: post #66

Earlier quoted context omitted.

Incorrect. Any growth in a system of finite resources is sigmoidal, with an exponential portion early in the curve before diminishing returns kicks in.

We've been promised this since the 90's, and, yet, we've been pushing back the wall Moore's law was supposed to hit for more than 30 years. And we haven't even properly started to play with non-transistor logic and analog neural devices, so I am cautiously imagining we'll remain exponential for the time being.

It all depends on where we are in the curve.

Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#86

Post by beisner »

carlmr wrote: post #46
data_maan wrote: post #34

Earlier quoted context omitted.

> Current hardware is easily up to the task. I don't think so. If you want to model a single synapse in full to capture all effects that might lead to "learning", you have a system of ordinary differential equations. Solving that is very hard, and solving that for 10 million neurons is impossible. On current hardware can only implement but a poor caricature of a real neuron.

While this is true, the complexity perspective misses something more fundamental. 1) Our brains, and moreso those of animals, come with a really good pretraining at birth. This is collective genetic knowledge of millions of generations distilled into your brain. 2) Our brains have a lot of sensors and actuators to interact with the world. We only learn by reading as adults when our brains can already do the synesthes…

While true, there’s a relatively small upper bound on how many bits of information are in this pre-training. Specifically, in the form of how much information is contained in DNA, which is only a couple gigabytes.

Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#87

Post by Animats »

data_maan wrote: post #34
Animats wrote: post #6

This is a common sentiment, and pundits have been making similar remarks for decades. This author writes "Sixty years later, however, high-level reasoning and thought remain elusive." That's the wrong problem with AI. The trouble with AI is that it still sucks at manipulation in unstructured situations and at "common sense". Common sense can usefully be defined as getting through the next 30 seconds of life without a…

> Current hardware is easily up to the task. I don't think so. If you want to model a single synapse in full to capture all effects that might lead to "learning", you have a system of ordinary differential equations. Solving that is very hard, and solving that for 10 million neurons is impossible. On current hardware can only implement but a poor caricature of a real neuron.

That problem has been overcome.[1]

This is a neat result. This research started with the differential equation model of a neuron and tried to train various neural nets to get the same result to within 99%. They succeeded. Worst case took an 8-layer net with 256 elements per layer. See Fig. 4. So, 10 billion elements for a squirrel. Not that big by current standards.

It's not clear that a model which tracks the biological neuron that accurately is needed. They discuss simpler models that are almost as good.

Low-end mammal brains should be buildable right now. It's not a hardware limitation.

[1] https://www.sciencedirect.com/science/article/pii/S089662732...

Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#89

Post by vt85 »

blitzar wrote: post #37
Ilverin wrote: post #3

I think this essay includes a specific prediction, that human level ai is far away, that might be disproved this decade. If human level ai is close, focusing on some other kind of ai is more likely to be a waste of time.

Microsoft already got to human level ai. Twitter taught Microsoft’s AI chatbot to be a racist asshole in less than a day. https://www.theverge.com/2016/3/24/11297050/tay-microsoft-ch...

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Re: Artificial Intelligence: The Revolution Hasn’t Happened Yet (2018)

#90

Post by lossolo »

beisner wrote: post #86
carlmr wrote: post #46

Earlier quoted context omitted.

While this is true, the complexity perspective misses something more fundamental. 1) Our brains, and moreso those of animals, come with a really good pretraining at birth. This is collective genetic knowledge of millions of generations distilled into your brain. 2) Our brains have a lot of sensors and actuators to interact with the world. We only learn by reading as adults when our brains can already do the synesthes…

While true, there’s a relatively small upper bound on how many bits of information are in this pre-training. Specifically, in the form of how much information is contained in DNA, which is only a couple gigabytes.

Stable diffusion model is around 4 gigabytes, inside that 4 gigabytes you have understanding of the whole english language model and mapping to billions of objects, people, concepts etc capable of generating from just a single sentence almost any picture in any style you imagine. Seems like a few gigabytes can hold a lot of information.
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